arXiv research

A new method for active learning works well across all label budgets.

A heuristic method refines search space for Bayesian optimization with low budget.

Adam is the most practical optimizer, especially in low-budget scenarios.

The paper improves PCS approximation for ranking and selection under limited simulation budgets.

Proposes a method to allocate time budgets in mixed criticality systems.

Optimal probing framework for scalable network monitoring.

Efficient method for generating adversarial examples with limited query budget.

GNMR controls runtime stability in low-precision language model training.

New method recovers matrix column space with active sampling for better results.

Bayesian approach improves AdaLoRA's performance and efficiency.

A new method optimizes Bayesian optimization in high dimensions by focusing on low-dimensional subspaces.

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversaria…

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified {\it average} feature acquisition b…

STR reparameterizes DNN weights with soft thresholds for better sparsity and accuracy.

NPAS trains neural networks with a fixed parameter budget, improving performance and compactness.

We investigate how to train kernel approximation methods that generalize well under a memory budget. Building on recent theoretical work, we define a measure of kernel approximation error which we find to be more predictive of the empirical generalization performance of kernel approximation methods than conventional me…

Engineering problems often involve data sources of variable fidelity with different costs of obtaining an observation. In particular, one can use both a cheap low fidelity function (e.g. a computational experiment with a CFD code) and an expensive high fidelity function (e.g. a wind tunnel experiment) to generate a dat…

Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks. When such methods are provided with sufficient training data, broadly-applicable classes of nonlinear functions can be approximated with desired accuracy. Nevertheless, inherent to the nonparametric nature of kernel…

Designs efficient factorial experiments for product design under budget constraints.

StatLoRA uses statistical inference to allocate ranks in LoRA fine-tuning, improving performance.

Estimates model performance based on compute budget and evaluates stability over time.

We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budg…

POCA optimizes hyperparameters with adaptive allocation for faster convergence.

DSI improves tail-risk estimation in generative models by averaging checkpoints.

Crowdsourcing platforms provide marketplaces where task requesters can pay to get labels on their data. Such markets have emerged recently as popular venues for collecting annotations that are crucial in training machine learning models in various applications. However, as jobs are tedious and payments are low, errors …

We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care prov…

FLAML automates model selection and hyperparameter tuning with low resource cost.

We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts, ie, both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. …

Bayesian neural networks improve simulation-based inference with limited data.

E$^2$CM uses class means for efficient early exits in neural networks.

New algorithm reduces regret and constraint violation in constrained bandit problems.

Active learning selects both observations and annotation precision for Gaussian Processes.

Framework insures AI actions with reserve capital, preventing loss.

Moniqua improves SGD convergence with quantized communication.

Georgia needs a new budget code to manage fiscal policies effectively.

We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy. To date, budget training is available only for primal (SGD-ba…

Efficiently simulates risk budgeting portfolios using novel algorithms.

Bridges uplift modeling and sequential decision-making with online budget allocation.

C3T-Budget optimizes drug efficacy in dose-finding trials with budget and safety constraints.

DSA efficiently allocates sparsity across layers for budgeted pruning.

The challenge of taking many variables into account in optimization problems may be overcome under the hypothesis of low effective dimensionality. Then, the search of solutions can be reduced to the random embedding of a low dimensional space into the original one, resulting in a more manageable optimization problem. S…

MPC outperforms reactive budgeting in non-stationary return environments.

Ahpatron improves online kernel learning with tighter mistake bounds.

Paper proves existence and computation of Risk Budgeting portfolios.

Framework ranks sectors influenced by Indian Union Budgets.

New algorithms improve best-arm identification with varying rewards.

New method reduces regret in budgeted learning problems.

This study analyzes how the Indian stock market reacts to budget announcements using fractal methods.